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Reliability Optimization of Urban Transit Brake System For Efficient Maintenance

효율적 유지보수를 위한 도시철도 전동차 브레이크의 시스템 신뢰도 최적화

  • 배철호 (성균관대학교 대학원 기계공학부) ;
  • 김현준 (성균관대학교 대학원 기계공학부) ;
  • 이정환 (성균관대학교 대학원 기계공학부) ;
  • 김세훈 (성균관대학교 대학원 기계공학부) ;
  • 이호용 (한국철도기술연구원) ;
  • 서명원 (성균관대학교 기계공학부)
  • Published : 2007.01.01

Abstract

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

Keywords

System Reliability Optimization;Urban Transit;Maintenence;Neural Network;Genetic Algorithm

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